Overview

Dataset statistics

Number of variables20
Number of observations295881
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.1 MiB
Average record size in memory160.0 B

Variable types

Numeric20

Alerts

CO (ppm) is highly overall correlated with Time (s) and 9 other fieldsHigh correlation
Heater voltage (V) is highly overall correlated with R4 (MOhm) and 7 other fieldsHigh correlation
R1 (MOhm) is highly overall correlated with R2 (MOhm) and 9 other fieldsHigh correlation
R2 (MOhm) is highly overall correlated with R1 (MOhm) and 6 other fieldsHigh correlation
R3 (MOhm) is highly overall correlated with R1 (MOhm) and 5 other fieldsHigh correlation
R4 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R5 (MOhm) is highly overall correlated with R1 (MOhm) and 12 other fieldsHigh correlation
R6 (MOhm) is highly overall correlated with R1 (MOhm) and 11 other fieldsHigh correlation
R7 (MOhm) is highly overall correlated with R1 (MOhm) and 12 other fieldsHigh correlation
R8 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R9 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R10 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R11 (MOhm) is highly overall correlated with CO (ppm) and 10 other fieldsHigh correlation
R12 (MOhm) is highly overall correlated with CO (ppm) and 13 other fieldsHigh correlation
R13 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R14 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
Time (s) is highly overall correlated with CO (ppm) and 2 other fieldsHigh correlation
Humidity (%r.h.) is highly overall correlated with Time (s) and 2 other fieldsHigh correlation
Temperature (C) is highly overall correlated with Time (s) and 2 other fieldsHigh correlation
Flow rate (mL/min) is highly skewed (γ1 = -103.5961668)Skewed
Time (s) is uniformly distributedUniform
Time (s) has unique valuesUnique
CO (ppm) has 32170 (10.9%) zerosZeros

Reproduction

Analysis started2022-12-20 08:46:14.809553
Analysis finished2022-12-20 08:48:04.178095
Duration1 minute and 49.37 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Time (s)
Real number (ℝ)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct295881
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45446.047
Minimum0
Maximum90910.13
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:18:04.336636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4551.252
Q122718.873
median45446.691
Q368168.338
95-th percentile86360.226
Maximum90910.13
Range90910.13
Interquartile range (IQR)45449.465

Descriptive statistics

Standard deviation26242.341
Coefficient of variation (CV)0.57743946
Kurtosis-1.2000861
Mean45446.047
Median Absolute Deviation (MAD)22724.74
Skewness0.00037939955
Sum1.3446622 × 1010
Variance6.8866046 × 108
MonotonicityStrictly increasing
2022-12-20T14:18:04.622628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
60628.849 1
 
< 0.1%
60600.428 1
 
< 0.1%
60600.122 1
 
< 0.1%
60599.812 1
 
< 0.1%
60599.503 1
 
< 0.1%
60599.194 1
 
< 0.1%
60598.884 1
 
< 0.1%
60598.575 1
 
< 0.1%
60598.267 1
 
< 0.1%
Other values (295871) 295871
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.308 1
< 0.1%
0.618 1
< 0.1%
0.925 1
< 0.1%
1.234 1
< 0.1%
1.54 1
< 0.1%
1.85 1
< 0.1%
2.157 1
< 0.1%
2.467 1
< 0.1%
2.775 1
< 0.1%
ValueCountFrequency (%)
90910.13 1
< 0.1%
90909.822 1
< 0.1%
90909.514 1
< 0.1%
90909.204 1
< 0.1%
90908.896 1
< 0.1%
90908.589 1
< 0.1%
90908.279 1
< 0.1%
90907.969 1
< 0.1%
90907.66 1
< 0.1%
90907.351 1
< 0.1%

CO (ppm)
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct302
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9003629
Minimum0
Maximum20
Zeros32170
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:18:04.810245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.44
median8.89
Q315.56
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)11.12

Descriptive statistics

Standard deviation6.4271612
Coefficient of variation (CV)0.6491844
Kurtosis-1.2331721
Mean9.9003629
Median Absolute Deviation (MAD)6.67
Skewness0.0092648747
Sum2929329.3
Variance41.308401
MonotonicityNot monotonic
2022-12-20T14:18:04.982962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32170
10.9%
8.89 29300
9.9%
2.22 29293
9.9%
13.33 29282
9.9%
6.67 29276
9.9%
20 29274
9.9%
15.56 29264
9.9%
4.44 29254
9.9%
17.78 29245
9.9%
11.11 29231
9.9%
Other values (292) 292
 
0.1%
ValueCountFrequency (%)
0 32170
10.9%
0.0644 1
 
< 0.1%
0.3023 1
 
< 0.1%
0.3333 1
 
< 0.1%
0.3996 1
 
< 0.1%
0.4662 1
 
< 0.1%
0.515 1
 
< 0.1%
0.7148 1
 
< 0.1%
0.7504 1
 
< 0.1%
0.8712 1
 
< 0.1%
ValueCountFrequency (%)
20 29274
9.9%
19.8489 1
 
< 0.1%
19.4139 1
 
< 0.1%
19.271 1
 
< 0.1%
18.8944 1
 
< 0.1%
18.7324 1
 
< 0.1%
18.6369 1
 
< 0.1%
18.32 1
 
< 0.1%
18.0464 1
 
< 0.1%
18.0005 1
 
< 0.1%

Humidity (%r.h.)
Real number (ℝ)

Distinct20212
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.984487
Minimum17.98
Maximum71.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:18:05.163060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum17.98
5-th percentile24.07
Q136.49
median47.04
Q355.58
95-th percentile64.9
Maximum71.61
Range53.63
Interquartile range (IQR)19.09

Descriptive statistics

Standard deviation12.22883
Coefficient of variation (CV)0.26593381
Kurtosis-0.7569144
Mean45.984487
Median Absolute Deviation (MAD)9.55
Skewness-0.19606805
Sum13605936
Variance149.54428
MonotonicityNot monotonic
2022-12-20T14:18:05.434809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.11 3298
 
1.1%
31.68 2983
 
1.0%
22.46 2497
 
0.8%
34.98 2374
 
0.8%
30.68 2320
 
0.8%
38.17 1783
 
0.6%
33.3 1769
 
0.6%
50.51 1768
 
0.6%
52.6 1750
 
0.6%
33.91 1657
 
0.6%
Other values (20202) 273682
92.5%
ValueCountFrequency (%)
17.98 59
< 0.1%
17.9802 1
 
< 0.1%
17.9804 1
 
< 0.1%
17.9805 1
 
< 0.1%
18.0164 1
 
< 0.1%
18.1131 1
 
< 0.1%
18.187 1
 
< 0.1%
18.2832 1
 
< 0.1%
18.3576 1
 
< 0.1%
18.4538 1
 
< 0.1%
ValueCountFrequency (%)
71.61 628
0.2%
71.6001 1
 
< 0.1%
71.5287 1
 
< 0.1%
71.4549 1
 
< 0.1%
71.3839 1
 
< 0.1%
71.3097 1
 
< 0.1%
71.2387 1
 
< 0.1%
71.1659 1
 
< 0.1%
71.14 596
0.2%
71.0892 1
 
< 0.1%

Temperature (C)
Real number (ℝ)

Distinct6303
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.047505
Minimum24.5
Maximum25.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:18:05.603317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum24.5
5-th percentile24.58
Q124.66
median24.98
Q325.46
95-th percentile25.58
Maximum25.74
Range1.24
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.37533939
Coefficient of variation (CV)0.014985101
Kurtosis-1.5148497
Mean25.047505
Median Absolute Deviation (MAD)0.36
Skewness0.15105859
Sum7411080.7
Variance0.14087966
MonotonicityNot monotonic
2022-12-20T14:18:05.782245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.62 36308
 
12.3%
25.5 22002
 
7.4%
25.54 20420
 
6.9%
24.58 18737
 
6.3%
24.86 18722
 
6.3%
25.58 17015
 
5.8%
24.66 12234
 
4.1%
25.18 11819
 
4.0%
25.14 11407
 
3.9%
24.54 10530
 
3.6%
Other values (6293) 116687
39.4%
ValueCountFrequency (%)
24.5 108
< 0.1%
24.5013 1
 
< 0.1%
24.5014 1
 
< 0.1%
24.5043 1
 
< 0.1%
24.5046 1
 
< 0.1%
24.505 1
 
< 0.1%
24.5052 1
 
< 0.1%
24.5055 1
 
< 0.1%
24.5057 1
 
< 0.1%
24.5064 1
 
< 0.1%
ValueCountFrequency (%)
25.74 468
0.2%
25.7384 1
 
< 0.1%
25.7333 1
 
< 0.1%
25.7314 1
 
< 0.1%
25.7312 1
 
< 0.1%
25.7311 1
 
< 0.1%
25.7308 1
 
< 0.1%
25.7306 1
 
< 0.1%
25.7286 1
 
< 0.1%
25.7285 1
 
< 0.1%

Flow rate (mL/min)
Real number (ℝ)

Distinct11129
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.94559
Minimum0
Maximum273.9747
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:18:05.955343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile239.7601
Q1239.8971
median239.9724
Q3240.0436
95-th percentile240.176
Maximum273.9747
Range273.9747
Interquartile range (IQR)0.1465

Descriptive statistics

Standard deviation1.9171754
Coefficient of variation (CV)0.0079900423
Kurtosis12149.767
Mean239.94559
Median Absolute Deviation (MAD)0.0731
Skewness-103.59617
Sum70995341
Variance3.6755615
MonotonicityNot monotonic
2022-12-20T14:18:06.115257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
239.979 140
 
< 0.1%
239.991 140
 
< 0.1%
239.9781 139
 
< 0.1%
239.972 138
 
< 0.1%
239.981 137
 
< 0.1%
239.9488 136
 
< 0.1%
239.9764 136
 
< 0.1%
239.9589 136
 
< 0.1%
239.983 135
 
< 0.1%
239.9941 134
 
< 0.1%
Other values (11119) 294510
99.5%
ValueCountFrequency (%)
0 10
< 0.1%
0.1283 1
 
< 0.1%
0.3495 1
 
< 0.1%
0.5729 1
 
< 0.1%
25.1466 1
 
< 0.1%
62.743 1
 
< 0.1%
87.8832 1
 
< 0.1%
92.2045 1
 
< 0.1%
99.3822 1
 
< 0.1%
106.3486 1
 
< 0.1%
ValueCountFrequency (%)
273.9747 1
< 0.1%
269.0562 1
< 0.1%
263.8508 1
< 0.1%
262.0881 1
< 0.1%
260.0087 1
< 0.1%
259.3095 1
< 0.1%
257.5254 1
< 0.1%
257.5135 1
< 0.1%
256.8696 1
< 0.1%
256.5111 1
< 0.1%

Heater voltage (V)
Real number (ℝ)

Distinct1758
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3550277
Minimum0.1982
Maximum0.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:18:06.287810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1982
5-th percentile0.1992
Q10.2
median0.2
Q30.207
95-th percentile0.8987
Maximum0.9
Range0.7018
Interquartile range (IQR)0.007

Descriptive statistics

Standard deviation0.28848925
Coefficient of variation (CV)0.81258236
Kurtosis-0.20778727
Mean0.3550277
Median Absolute Deviation (MAD)0.0001
Skewness1.3364058
Sum105045.95
Variance0.083226047
MonotonicityNot monotonic
2022-12-20T14:18:06.450596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 142814
48.3%
0.898 20836
 
7.0%
0.899 10477
 
3.5%
0.199 7979
 
2.7%
0.201 5842
 
2.0%
0.1999 4936
 
1.7%
0.1991 4878
 
1.6%
0.1995 4866
 
1.6%
0.1997 4855
 
1.6%
0.1998 4818
 
1.6%
Other values (1748) 83580
28.2%
ValueCountFrequency (%)
0.1982 2
 
< 0.1%
0.1983 1
 
< 0.1%
0.1984 1
 
< 0.1%
0.1987 2
 
< 0.1%
0.1988 5
 
< 0.1%
0.1989 5
 
< 0.1%
0.199 7979
2.7%
0.1991 4878
1.6%
0.1992 4808
1.6%
0.1993 4811
1.6%
ValueCountFrequency (%)
0.9 22
< 0.1%
0.8999 33
< 0.1%
0.8998 36
< 0.1%
0.8997 42
< 0.1%
0.8996 27
< 0.1%
0.8995 33
< 0.1%
0.8994 32
< 0.1%
0.8993 28
< 0.1%
0.8992 37
< 0.1%
0.8991 40
< 0.1%

R1 (MOhm)
Real number (ℝ)

Distinct8515
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.465344
Minimum0.0329
Maximum122.8846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:18:06.624308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0329
5-th percentile0.0792
Q10.4372
median2.2747
Q331.553
95-th percentile73.1111
Maximum122.8846
Range122.8517
Interquartile range (IQR)31.1158

Descriptive statistics

Standard deviation24.914118
Coefficient of variation (CV)1.4264888
Kurtosis0.69420621
Mean17.465344
Median Absolute Deviation (MAD)2.1928
Skewness1.3710209
Sum5167663.5
Variance620.71328
MonotonicityNot monotonic
2022-12-20T14:18:06.799165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.3332 881
 
0.3%
76.9383 814
 
0.3%
77.677 804
 
0.3%
75.0345 784
 
0.3%
78.3034 765
 
0.3%
75.6194 762
 
0.3%
73.7788 758
 
0.3%
74.3444 736
 
0.2%
73.1111 688
 
0.2%
79.0683 685
 
0.2%
Other values (8505) 288204
97.4%
ValueCountFrequency (%)
0.0329 1
 
< 0.1%
0.0334 1
 
< 0.1%
0.034 1
 
< 0.1%
0.0341 1
 
< 0.1%
0.0342 1
 
< 0.1%
0.0344 3
< 0.1%
0.0345 1
 
< 0.1%
0.0347 2
< 0.1%
0.0348 1
 
< 0.1%
0.0349 1
 
< 0.1%
ValueCountFrequency (%)
122.8846 1
 
< 0.1%
119.5851 1
 
< 0.1%
116.4568 7
 
< 0.1%
113.4868 9
 
< 0.1%
111.9292 9
 
< 0.1%
110.6632 14
 
< 0.1%
109.181 28
< 0.1%
107.9756 30
< 0.1%
106.5634 40
< 0.1%
105.4143 45
< 0.1%

R2 (MOhm)
Real number (ℝ)

Distinct8190
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.433934
Minimum0.0581
Maximum130.0566
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:18:06.971476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0581
5-th percentile0.1413
Q10.5125
median1.7048
Q335.9202
95-th percentile81.1822
Maximum130.0566
Range129.9985
Interquartile range (IQR)35.4077

Descriptive statistics

Standard deviation28.403654
Coefficient of variation (CV)1.4615494
Kurtosis0.17421585
Mean19.433934
Median Absolute Deviation (MAD)1.5622
Skewness1.2780584
Sum5750131.7
Variance806.76757
MonotonicityNot monotonic
2022-12-20T14:18:07.147270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82.7015 1214
 
0.4%
84.278 1174
 
0.4%
81.1822 1169
 
0.4%
83.5541 1165
 
0.4%
82.004 1165
 
0.4%
80.5097 1135
 
0.4%
85.1632 1131
 
0.4%
79.7171 1067
 
0.4%
79.0683 1013
 
0.3%
85.915 970
 
0.3%
Other values (8180) 284678
96.2%
ValueCountFrequency (%)
0.0581 1
< 0.1%
0.0587 1
< 0.1%
0.0606 1
< 0.1%
0.0608 2
< 0.1%
0.0611 1
< 0.1%
0.0618 1
< 0.1%
0.0621 2
< 0.1%
0.0622 2
< 0.1%
0.0626 2
< 0.1%
0.0628 1
< 0.1%
ValueCountFrequency (%)
130.0566 1
 
< 0.1%
126.3697 1
 
< 0.1%
121.0628 1
 
< 0.1%
119.5851 2
 
< 0.1%
117.8584 3
 
< 0.1%
116.4568 4
 
< 0.1%
114.818 2
 
< 0.1%
113.4868 8
 
< 0.1%
111.9292 16
< 0.1%
110.6632 28
< 0.1%

R3 (MOhm)
Real number (ℝ)

Distinct8221
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.655438
Minimum0.0549
Maximum159.2042
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:18:07.327406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0549
5-th percentile0.1119
Q10.626
median5.1667
Q348.1171
95-th percentile83.7703
Maximum159.2042
Range159.1493
Interquartile range (IQR)47.4911

Descriptive statistics

Standard deviation29.541469
Coefficient of variation (CV)1.2488236
Kurtosis-0.47826597
Mean23.655438
Median Absolute Deviation (MAD)5.053
Skewness0.97725295
Sum6999194.7
Variance872.69836
MonotonicityNot monotonic
2022-12-20T14:18:07.495943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84.6501 1212
 
0.4%
85.3974 1204
 
0.4%
86.3116 1195
 
0.4%
83.0507 1186
 
0.4%
83.7703 1166
 
0.4%
87.0883 1161
 
0.4%
82.2033 1086
 
0.4%
88.0388 1057
 
0.4%
81.51 1053
 
0.4%
80.0247 1004
 
0.3%
Other values (8211) 284557
96.2%
ValueCountFrequency (%)
0.0549 1
< 0.1%
0.0553 1
< 0.1%
0.0555 1
< 0.1%
0.0566 1
< 0.1%
0.0568 1
< 0.1%
0.0569 1
< 0.1%
0.057 1
< 0.1%
0.0572 1
< 0.1%
0.0573 1
< 0.1%
0.0576 2
< 0.1%
ValueCountFrequency (%)
159.2042 1
 
< 0.1%
151.3177 1
 
< 0.1%
120.3335 1
 
< 0.1%
112.8031 6
 
< 0.1%
111.2549 18
 
< 0.1%
109.9965 29
 
< 0.1%
108.5233 56
< 0.1%
107.3251 55
< 0.1%
105.9214 86
< 0.1%
104.7792 113
< 0.1%

R4 (MOhm)
Real number (ℝ)

Distinct7544
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.63101
Minimum0.0392
Maximum87.5664
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:18:07.789185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0392
5-th percentile0.1017
Q12.2828
median24.9762
Q338.0062
95-th percentile55.3201
Maximum87.5664
Range87.5272
Interquartile range (IQR)35.7234

Descriptive statistics

Standard deviation19.103361
Coefficient of variation (CV)0.80840224
Kurtosis-0.96929616
Mean23.63101
Median Absolute Deviation (MAD)16.8545
Skewness0.2591595
Sum6991966.8
Variance364.93841
MonotonicityNot monotonic
2022-12-20T14:18:07.944725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.4095 1181
 
0.4%
38.585 1172
 
0.4%
36.3021 1167
 
0.4%
36.8645 1165
 
0.4%
38.1853 1160
 
0.4%
38.8066 1151
 
0.4%
37.2375 1144
 
0.4%
38.4023 1131
 
0.4%
36.6641 1129
 
0.4%
36.1399 1129
 
0.4%
Other values (7534) 284352
96.1%
ValueCountFrequency (%)
0.0392 1
 
< 0.1%
0.0398 1
 
< 0.1%
0.0401 2
< 0.1%
0.0403 1
 
< 0.1%
0.0404 1
 
< 0.1%
0.0405 1
 
< 0.1%
0.0406 1
 
< 0.1%
0.0407 1
 
< 0.1%
0.041 2
< 0.1%
0.0411 3
< 0.1%
ValueCountFrequency (%)
87.5664 2
 
< 0.1%
85.5816 2
 
< 0.1%
84.5361 2
 
< 0.1%
83.6839 16
 
< 0.1%
82.6836 11
 
< 0.1%
81.8678 38
< 0.1%
80.9098 38
< 0.1%
80.1281 48
< 0.1%
79.2097 58
< 0.1%
78.4601 44
< 0.1%

R5 (MOhm)
Real number (ℝ)

Distinct7791
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.885354
Minimum0.0487
Maximum213.2301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:18:08.113838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0487
5-th percentile0.1152
Q12.075
median36.629
Q354.3512
95-th percentile81.8393
Maximum213.2301
Range213.1814
Interquartile range (IQR)52.2762

Descriptive statistics

Standard deviation28.317778
Coefficient of variation (CV)0.83569372
Kurtosis-1.0253043
Mean33.885354
Median Absolute Deviation (MAD)25.932
Skewness0.28289761
Sum10026032
Variance801.89654
MonotonicityNot monotonic
2022-12-20T14:18:08.275951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.8849 1472
 
0.5%
49.9804 1461
 
0.5%
49.7203 1455
 
0.5%
52.1043 1445
 
0.5%
50.296 1429
 
0.5%
53.0277 1427
 
0.5%
49.4117 1423
 
0.5%
51.4875 1415
 
0.5%
51.1571 1397
 
0.5%
49.1574 1388
 
0.5%
Other values (7781) 281569
95.2%
ValueCountFrequency (%)
0.0487 1
< 0.1%
0.0488 1
< 0.1%
0.0494 1
< 0.1%
0.0496 1
< 0.1%
0.0501 2
< 0.1%
0.0502 1
< 0.1%
0.0503 2
< 0.1%
0.0505 1
< 0.1%
0.0507 2
< 0.1%
0.0508 1
< 0.1%
ValueCountFrequency (%)
213.2301 1
 
< 0.1%
169.2115 1
 
< 0.1%
127.7625 1
 
< 0.1%
126.116 3
 
< 0.1%
124.1949 10
 
< 0.1%
122.6378 8
 
< 0.1%
120.8197 11
 
< 0.1%
119.345 8
 
< 0.1%
117.6218 17
< 0.1%
116.223 30
< 0.1%

R6 (MOhm)
Real number (ℝ)

Distinct7769
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.411745
Minimum0.0473
Maximum183.1201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:18:08.444825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0473
5-th percentile0.124
Q11.7326
median26.3722
Q351.7707
95-th percentile80.6531
Maximum183.1201
Range183.0728
Interquartile range (IQR)50.0381

Descriptive statistics

Standard deviation27.963244
Coefficient of variation (CV)0.91948833
Kurtosis-0.95378077
Mean30.411745
Median Absolute Deviation (MAD)24.9386
Skewness0.48557115
Sum8998257.4
Variance781.94302
MonotonicityNot monotonic
2022-12-20T14:18:08.611207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.4753 1281
 
0.4%
50.2138 1264
 
0.4%
50.8369 1254
 
0.4%
49.6055 1250
 
0.4%
48.6935 1229
 
0.4%
49.8802 1221
 
0.4%
51.7707 1216
 
0.4%
49.0116 1211
 
0.4%
50.4951 1195
 
0.4%
48.1206 1194
 
0.4%
Other values (7759) 283566
95.8%
ValueCountFrequency (%)
0.0473 1
< 0.1%
0.0486 1
< 0.1%
0.0493 1
< 0.1%
0.0497 1
< 0.1%
0.0503 1
< 0.1%
0.0504 1
< 0.1%
0.0505 1
< 0.1%
0.0507 1
< 0.1%
0.0508 1
< 0.1%
0.0511 1
< 0.1%
ValueCountFrequency (%)
183.1201 1
 
< 0.1%
142.9112 1
 
< 0.1%
135.7609 1
 
< 0.1%
116.8232 1
 
< 0.1%
115.3585 3
 
< 0.1%
113.6483 9
 
< 0.1%
112.2611 13
 
< 0.1%
110.6402 41
< 0.1%
109.3244 65
< 0.1%
107.7859 96
< 0.1%

R7 (MOhm)
Real number (ℝ)

Distinct7670
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.992486
Minimum0.0522
Maximum174.8127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:18:08.792598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0522
5-th percentile0.1226
Q12.2217
median35.9328
Q355.0846
95-th percentile82.883
Maximum174.8127
Range174.7605
Interquartile range (IQR)52.8629

Descriptive statistics

Standard deviation28.489715
Coefficient of variation (CV)0.83811802
Kurtosis-1.0821033
Mean33.992486
Median Absolute Deviation (MAD)26.2506
Skewness0.28064596
Sum10057731
Variance811.66386
MonotonicityNot monotonic
2022-12-20T14:18:08.957804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.7185 1489
 
0.5%
50.8483 1479
 
0.5%
52.0732 1451
 
0.5%
51.7898 1434
 
0.5%
53.3574 1434
 
0.5%
53.0601 1430
 
0.5%
50.5778 1425
 
0.5%
51.1767 1424
 
0.5%
52.7077 1412
 
0.5%
49.6787 1403
 
0.5%
Other values (7660) 281500
95.1%
ValueCountFrequency (%)
0.0522 1
< 0.1%
0.0528 1
< 0.1%
0.0536 1
< 0.1%
0.0538 2
< 0.1%
0.0541 1
< 0.1%
0.0544 1
< 0.1%
0.0545 1
< 0.1%
0.0546 1
< 0.1%
0.0547 1
< 0.1%
0.0552 1
< 0.1%
ValueCountFrequency (%)
174.8127 1
 
< 0.1%
116.9118 1
 
< 0.1%
115.5214 4
 
< 0.1%
113.8957 5
 
< 0.1%
112.5752 15
 
< 0.1%
111.0301 27
 
< 0.1%
109.7743 64
< 0.1%
108.304 106
< 0.1%
107.1083 117
< 0.1%
105.7075 141
< 0.1%

R8 (MOhm)
Real number (ℝ)

Distinct6188
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.469294
Minimum0.0337
Maximum124.7718
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:18:09.133897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0337
5-th percentile0.0998
Q113.0077
median29.6461
Q344.2479
95-th percentile65.5821
Maximum124.7718
Range124.7381
Interquartile range (IQR)31.2402

Descriptive statistics

Standard deviation21.363598
Coefficient of variation (CV)0.72494434
Kurtosis-0.76488539
Mean29.469294
Median Absolute Deviation (MAD)14.9946
Skewness0.14808732
Sum8719404.2
Variance456.40331
MonotonicityNot monotonic
2022-12-20T14:18:09.305609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.6564 1104
 
0.4%
42.823 1089
 
0.4%
42.5736 1080
 
0.4%
44.4719 1076
 
0.4%
42.1236 1073
 
0.4%
56.7773 1067
 
0.4%
50.3742 1066
 
0.4%
53.3555 1065
 
0.4%
50.0315 1065
 
0.4%
51.6712 1062
 
0.4%
Other values (6178) 285134
96.4%
ValueCountFrequency (%)
0.0337 1
 
< 0.1%
0.0341 1
 
< 0.1%
0.0343 1
 
< 0.1%
0.0344 1
 
< 0.1%
0.0345 1
 
< 0.1%
0.0346 1
 
< 0.1%
0.0347 1
 
< 0.1%
0.0348 2
< 0.1%
0.0349 2
< 0.1%
0.035 4
< 0.1%
ValueCountFrequency (%)
124.7718 1
 
< 0.1%
109.8598 1
 
< 0.1%
104.2574 1
 
< 0.1%
101.6635 2
 
< 0.1%
100.3018 3
 
< 0.1%
99.1944 2
 
< 0.1%
97.8971 4
 
< 0.1%
96.8414 8
< 0.1%
95.604 8
< 0.1%
94.5965 14
< 0.1%

R9 (MOhm)
Real number (ℝ)

Distinct6237
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.073508
Minimum0.0289
Maximum112.0602
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:18:09.481278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0289
5-th percentile0.0968
Q18.6245
median23.0885
Q338.5113
95-th percentile62.2706
Maximum112.0602
Range112.0313
Interquartile range (IQR)29.8868

Descriptive statistics

Standard deviation19.927259
Coefficient of variation (CV)0.79475353
Kurtosis-0.60607836
Mean25.073508
Median Absolute Deviation (MAD)15.2265
Skewness0.45063267
Sum7418774.6
Variance397.09565
MonotonicityNot monotonic
2022-12-20T14:18:09.646010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0975 2821
 
1.0%
0.0973 2689
 
0.9%
0.0977 2682
 
0.9%
0.0976 2677
 
0.9%
0.0972 2629
 
0.9%
0.0971 2515
 
0.9%
0.0978 2393
 
0.8%
0.097 2325
 
0.8%
0.098 2251
 
0.8%
0.0981 2031
 
0.7%
Other values (6227) 270868
91.5%
ValueCountFrequency (%)
0.0289 1
 
< 0.1%
0.0293 2
< 0.1%
0.0296 2
< 0.1%
0.0297 3
< 0.1%
0.0298 3
< 0.1%
0.0299 3
< 0.1%
0.03 4
< 0.1%
0.0301 2
< 0.1%
0.0302 2
< 0.1%
0.0303 1
 
< 0.1%
ValueCountFrequency (%)
112.0602 2
 
< 0.1%
92.4455 1
 
< 0.1%
90.4623 1
 
< 0.1%
89.5884 1
 
< 0.1%
88.5615 1
 
< 0.1%
85.9337 2
 
< 0.1%
84.9877 2
 
< 0.1%
84.2149 3
 
< 0.1%
83.3057 8
< 0.1%
82.5627 14
< 0.1%

R10 (MOhm)
Real number (ℝ)

Distinct6466
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.475511
Minimum0.0373
Maximum105.9214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:18:09.822431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0373
5-th percentile0.1184
Q18.2176
median25.0764
Q341.2095
95-th percentile62.7987
Maximum105.9214
Range105.8841
Interquartile range (IQR)32.9919

Descriptive statistics

Standard deviation20.612592
Coefficient of variation (CV)0.77855316
Kurtosis-0.81608773
Mean26.475511
Median Absolute Deviation (MAD)16.3501
Skewness0.32760852
Sum7833600.6
Variance424.87897
MonotonicityNot monotonic
2022-12-20T14:18:10.090410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1205 1776
 
0.6%
0.1204 1772
 
0.6%
0.1206 1771
 
0.6%
0.1201 1716
 
0.6%
0.1208 1698
 
0.6%
0.1202 1664
 
0.6%
0.1209 1608
 
0.5%
0.1199 1538
 
0.5%
0.1198 1452
 
0.5%
0.121 1443
 
0.5%
Other values (6456) 279443
94.4%
ValueCountFrequency (%)
0.0373 1
< 0.1%
0.0379 2
< 0.1%
0.0383 1
< 0.1%
0.0384 2
< 0.1%
0.0385 1
< 0.1%
0.0386 1
< 0.1%
0.0387 1
< 0.1%
0.0388 2
< 0.1%
0.039 1
< 0.1%
0.0392 1
< 0.1%
ValueCountFrequency (%)
105.9214 1
 
< 0.1%
102.3503 1
 
< 0.1%
95.6897 1
 
< 0.1%
92.5828 2
 
< 0.1%
91.7066 1
 
< 0.1%
90.6767 4
 
< 0.1%
89.8357 5
 
< 0.1%
88.8467 3
 
< 0.1%
88.0388 13
< 0.1%
87.0883 8
< 0.1%

R11 (MOhm)
Real number (ℝ)

Distinct6208
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.603242
Minimum0.0309
Maximum98.1088
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:18:10.261383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0309
5-th percentile0.1084
Q111.3149
median29.693
Q345.2361
95-th percentile66.8349
Maximum98.1088
Range98.0779
Interquartile range (IQR)33.9212

Descriptive statistics

Standard deviation21.798691
Coefficient of variation (CV)0.7363616
Kurtosis-0.83216787
Mean29.603242
Median Absolute Deviation (MAD)16.0214
Skewness0.17271482
Sum8759037
Variance475.18293
MonotonicityNot monotonic
2022-12-20T14:18:10.429612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1093 2799
 
0.9%
0.1094 2750
 
0.9%
0.1092 2699
 
0.9%
0.109 2630
 
0.9%
0.1089 2506
 
0.8%
0.1088 2288
 
0.8%
0.1096 2242
 
0.8%
0.1086 2097
 
0.7%
0.1085 1835
 
0.6%
0.1097 1812
 
0.6%
Other values (6198) 272223
92.0%
ValueCountFrequency (%)
0.0309 1
 
< 0.1%
0.0315 1
 
< 0.1%
0.0317 2
 
< 0.1%
0.0318 2
 
< 0.1%
0.0319 2
 
< 0.1%
0.032 2
 
< 0.1%
0.0321 1
 
< 0.1%
0.0323 1
 
< 0.1%
0.0324 3
< 0.1%
0.0325 5
< 0.1%
ValueCountFrequency (%)
98.1088 1
 
< 0.1%
95.9797 1
 
< 0.1%
94.8564 1
 
< 0.1%
93.9401 2
 
< 0.1%
92.8633 15
 
< 0.1%
91.9845 15
 
< 0.1%
90.9514 21
 
< 0.1%
90.1079 37
< 0.1%
89.1159 37
< 0.1%
88.3056 71
< 0.1%

R12 (MOhm)
Real number (ℝ)

Distinct6330
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.392214
Minimum0.0335
Maximum97.1312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:18:10.602672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0335
5-th percentile0.108
Q110.3634
median27.8791
Q342.0413
95-th percentile59.235
Maximum97.1312
Range97.0977
Interquartile range (IQR)31.6779

Descriptive statistics

Standard deviation19.880485
Coefficient of variation (CV)0.72577135
Kurtosis-0.83278442
Mean27.392214
Median Absolute Deviation (MAD)14.8165
Skewness0.1123428
Sum8104835.8
Variance395.23366
MonotonicityNot monotonic
2022-12-20T14:18:10.770587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1094 1273
 
0.4%
0.1095 1262
 
0.4%
0.1093 1219
 
0.4%
0.1096 1185
 
0.4%
0.1098 1177
 
0.4%
0.1091 1174
 
0.4%
0.1099 1141
 
0.4%
41.2376 1106
 
0.4%
50.3466 1097
 
0.4%
0.1101 1094
 
0.4%
Other values (6320) 284153
96.0%
ValueCountFrequency (%)
0.0335 1
< 0.1%
0.0337 1
< 0.1%
0.0338 1
< 0.1%
0.034 2
< 0.1%
0.0341 1
< 0.1%
0.0342 2
< 0.1%
0.0346 1
< 0.1%
0.0347 2
< 0.1%
0.0349 2
< 0.1%
0.0351 2
< 0.1%
ValueCountFrequency (%)
97.1312 1
 
< 0.1%
92.1698 2
 
< 0.1%
91.1346 1
 
< 0.1%
90.2894 4
 
< 0.1%
89.2954 9
 
< 0.1%
88.4834 14
 
< 0.1%
87.5281 25
< 0.1%
86.7475 38
< 0.1%
85.8287 29
< 0.1%
85.0777 53
< 0.1%

R13 (MOhm)
Real number (ℝ)

Distinct6366
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.173082
Minimum0.0334
Maximum93.9967
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:18:10.941989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0334
5-th percentile0.1018
Q18.5177
median24.1951
Q338.9187
95-th percentile58.9429
Maximum93.9967
Range93.9633
Interquartile range (IQR)30.401

Descriptive statistics

Standard deviation19.268826
Coefficient of variation (CV)0.76545359
Kurtosis-0.7594048
Mean25.173082
Median Absolute Deviation (MAD)14.886
Skewness0.3016093
Sum7448236.6
Variance371.28764
MonotonicityNot monotonic
2022-12-20T14:18:11.103782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1037 1244
 
0.4%
0.1035 1141
 
0.4%
0.104 1139
 
0.4%
0.1042 1131
 
0.4%
0.1038 1118
 
0.4%
0.1039 1106
 
0.4%
0.1034 1102
 
0.4%
0.1043 1098
 
0.4%
0.1044 1096
 
0.4%
0.1032 1093
 
0.4%
Other values (6356) 284613
96.2%
ValueCountFrequency (%)
0.0334 1
< 0.1%
0.0339 1
< 0.1%
0.0341 1
< 0.1%
0.0343 1
< 0.1%
0.0352 2
< 0.1%
0.0353 1
< 0.1%
0.0355 1
< 0.1%
0.0356 2
< 0.1%
0.0357 2
< 0.1%
0.0358 1
< 0.1%
ValueCountFrequency (%)
93.9967 1
 
< 0.1%
86.5605 1
 
< 0.1%
84.1371 1
 
< 0.1%
83.2626 2
 
< 0.1%
82.5474 2
 
< 0.1%
81.7051 5
 
< 0.1%
81.016 7
 
< 0.1%
80.2041 19
< 0.1%
79.5397 18
< 0.1%
78.7567 37
< 0.1%

R14 (MOhm)
Real number (ℝ)

Distinct6195
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.150379
Minimum0.0322
Maximum95.4717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:18:11.272046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0322
5-th percentile0.1072
Q110.2841
median28.8391
Q346.7759
95-th percentile70.6179
Maximum95.4717
Range95.4395
Interquartile range (IQR)36.4918

Descriptive statistics

Standard deviation22.995919
Coefficient of variation (CV)0.76270744
Kurtosis-0.92719631
Mean30.150379
Median Absolute Deviation (MAD)18.1711
Skewness0.25045789
Sum8920924.3
Variance528.81227
MonotonicityNot monotonic
2022-12-20T14:18:11.440323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.108 2507
 
0.8%
0.1082 2490
 
0.8%
0.1083 2384
 
0.8%
0.1079 2274
 
0.8%
0.1084 2146
 
0.7%
0.1078 2112
 
0.7%
0.1086 2072
 
0.7%
0.1077 2065
 
0.7%
0.1087 2025
 
0.7%
0.1075 1953
 
0.7%
Other values (6185) 273853
92.6%
ValueCountFrequency (%)
0.0322 1
 
< 0.1%
0.0326 1
 
< 0.1%
0.0327 2
 
< 0.1%
0.0328 1
 
< 0.1%
0.033 5
< 0.1%
0.0332 1
 
< 0.1%
0.0333 5
< 0.1%
0.0334 4
< 0.1%
0.0335 4
< 0.1%
0.0336 7
< 0.1%
ValueCountFrequency (%)
95.4717 1
 
< 0.1%
93.4235 1
 
< 0.1%
92.521 1
 
< 0.1%
91.4606 9
 
< 0.1%
90.595 2
 
< 0.1%
89.5776 7
 
< 0.1%
88.7468 3
 
< 0.1%
87.7697 10
 
< 0.1%
86.9716 15
 
< 0.1%
86.0327 42
< 0.1%

Interactions

2022-12-20T14:17:58.247668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:49.449298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:52.822411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:56.328733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:59.822961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:03.423797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:07.011277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:10.763390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:14.279361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:17.858290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:21.672082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:25.418875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:29.103832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:32.812866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:36.600009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:40.286579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:43.836050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:47.484231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:51.055608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:54.706770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:58.423261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:49.590453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:52.985616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:56.492303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:59.975346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:03.674721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:07.181348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:10.929177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:14.440926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:18.034626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:21.834135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:25.594638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:29.280640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:32.986488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:36.775832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:40.456120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:44.003472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:47.651158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:51.226425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:54.871474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:58.618370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:49.764081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:53.166892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:56.667725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:00.160429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:03.857408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:07.367462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:11.117281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:14.629813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:18.225238image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:22.014411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:25.780166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:29.473568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:33.185313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:36.973630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:40.648127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:44.197846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:47.844733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:51.419106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:55.059800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:58.790460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:49.929752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:53.337441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:56.841002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:00.318322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:04.023495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:07.542524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:11.296183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:14.796767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:18.460373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:22.184766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:25.959237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:29.657439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:33.363993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:37.144990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:40.828285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:44.368628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:48.022336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:51.599904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:55.229759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:58.982387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:50.100970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:53.511446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:57.017588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:00.590564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:04.196751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:07.722307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:11.475245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:14.975410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:18.665759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:22.377984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:26.139109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:29.846618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:33.552361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:37.333454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:41.009086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:44.556035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:48.209133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:51.779552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:55.410786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:59.156140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:50.262412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:53.685435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:57.287742image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:00.789096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:04.364006image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:07.917063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:11.652050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:15.148880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:18.849626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:22.705815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:26.329015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:30.034501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:33.729227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:37.512375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:41.184171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:44.727340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:48.385624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:51.965712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:55.584298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:59.441662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:50.433523image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:53.871358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:57.465394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:00.971342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:04.546502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:08.153621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:11.835985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:15.334845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:19.042051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:22.903755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:26.518302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:30.228638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:33.923535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:37.702164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:41.377916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:44.918455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:48.578799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:52.149608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:55.768179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:59.623771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:50.601771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:54.050206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:57.637991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:01.152902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:04.711991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:08.354536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:12.011528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:15.594326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:19.224738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:23.085119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:26.701069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:30.415936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:34.107031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:37.888435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:41.557583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:45.096116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:48.755838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:52.333163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:55.948014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:59.803802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:50.762944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:54.227956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:57.813551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:01.332253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-20T14:17:03.097769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:06.675322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:10.417474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:13.933100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:17.525283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:21.218875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:25.082735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:28.765607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:32.453367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:36.243011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:39.861361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:43.493371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:47.148338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:50.710457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:54.372362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:57.903068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:18:01.786262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:52.661196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:56.146261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:16:59.657548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:03.255201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:06.837515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:10.586666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:14.106456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:17.692037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:21.494686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:25.247337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:28.935478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:32.625594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:36.422772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:40.120576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:43.662245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:47.313169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:50.882655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:54.538069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:17:58.072343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-20T14:18:11.604838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-20T14:18:11.891361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-20T14:18:12.281519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-20T14:18:12.557901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-20T14:18:12.845968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-20T14:18:02.081833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-20T14:18:02.864965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
00.0000.057.7125.74243.78410.19960.78380.75391.407714.955224.018612.855621.334645.250539.044645.358354.668443.306040.016055.5538
10.3080.057.7125.74242.04500.19920.74220.72961.318414.419822.706511.686519.785846.298637.455345.358353.627542.228339.081153.4763
20.6180.057.7125.74241.63040.20000.70750.70881.237813.222820.766110.608218.233844.743737.801044.632149.362042.228339.812055.2289
30.9250.057.7125.74241.21980.20000.67690.68971.170412.445519.43359.685217.107245.484636.782143.969148.806442.454839.081154.5271
41.2340.057.7125.74240.82820.20000.64980.67321.111911.809917.86038.796515.757843.033139.044644.174249.930142.228338.918753.8426
51.5400.057.7125.74240.89390.20000.62660.65681.059710.900616.54318.121614.411042.123636.454344.632152.974442.876337.878652.8174
61.8500.057.7125.74240.96050.20000.60470.64301.013610.256815.01767.352513.191147.091336.132142.016148.019841.023638.032753.4763
72.1570.057.7125.74241.02640.19980.58570.62980.97429.638314.03026.830812.098141.922136.602643.088452.051941.417637.363652.5231
82.4670.057.7125.74241.01720.20000.56820.61840.93768.920112.77336.228711.209545.250535.033241.830148.263040.846938.376053.4763
92.7750.057.7125.74240.98890.20000.55220.60730.90598.379612.07465.743610.175843.762736.782143.285552.051939.884237.214851.8869
Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
29587190907.3510.062.0625.10.00.89900.09300.14950.12680.10790.12440.12980.13350.11180.10000.12390.11170.11600.11000.1110
29587290907.6600.062.0625.10.00.89820.09400.15070.12810.10850.12550.13040.13440.11180.10010.12390.11150.11600.11020.1110
29587390907.9690.062.0625.10.00.89800.09500.15150.12920.10930.12650.13100.13540.11180.10010.12390.11130.11590.11000.1110
29587490908.2790.062.0625.10.00.89800.09570.15230.13030.10970.12750.13160.13620.11190.10010.12380.11130.11580.11020.1110
29587590908.5890.062.0625.10.00.89820.09650.15310.13120.11030.05810.07300.11310.11190.10010.12380.11130.06230.11100.1200
29587690908.8960.062.0625.10.00.89810.20180.56070.86680.63310.98591.13671.62561.00651.09461.48492.67804.47806.07287.6403
29587790909.2040.062.0625.10.00.21192.14975.17277.99695.49287.99338.932011.927023.748121.168823.859433.295738.457837.037645.7715
29587890909.5140.062.0625.10.00.207711.750423.866733.722519.781331.132733.314141.126266.662555.624160.196760.760764.037758.942971.7899
29587990909.8220.062.0625.10.00.204034.908852.494964.141633.640059.175562.411969.684473.962365.184370.335677.443073.704761.931674.9537
29588090910.1300.062.0625.10.00.202456.576373.778883.050741.166979.557178.436878.433274.580068.379573.334379.477073.704761.531973.6785